Transcript Face Recognition

Face Recognition
A Literature Review
By Xiaozhen Niu
Department of Computing Science
Contents
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Face Segmentation/Detection
Facial Feature extraction
Face Recognition
Video-based Face Recognition
Comparison
Summary
Reference
Face Segmentation/Detection
Before the middle 90’s, the research
attention was only focused on singleface segmentation. The approaches
included:
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Deformable feature-based template
Neural network
Using skin color
Face Segmentation/Detection
During the past ten years, considerable
progress has been made in multi-face
recognition area, includes:
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Example-based learning approach by Sung
and Poggio (1994).
The neural network approach by Rowley et
al. (1998).
Support vector machine (SVM) by Osuna et
al. (1997).
Example-based learning approach (EBL)
Three parts:
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The image is divided into many possibleoverlapping windows, each window
pattern gets classified as either “a face”
or “not a face” based on a set of local
image measurements.
For each new pattern to be classified,
the system computes a set of different
measurements between the new pattern
and the canonical face model.
A trained classifier identifies the new
pattern as “a face” or “not a face”.
Example of a system using EBL
Neural network (NN)
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Kanade et al. first proposed an NNbased approach in 1996.
Although NN have received significant
attention in many research areas, few
applications were successful.
Why?
Neural network (NN)
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It’s easy to train a neural network with
samples which contain faces, but it is
much harder to train a neural network
with samples which do not.
The number of “non-face” simples are
just too large.
Neural network (NN)
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Neural network-based filter. A small
filter window is used to scan
through all portions of the image,
and to detect whether a face exists
in each window.
Merging overlapping detections
and arbitration. By setting a small
threshold, many false detections
can be eliminated.
An example of using NN
Test results of using NN
SVM
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SVM was first proposed in 1997, it
can be viewed as a way to train
polynomial neural network or radial
basic function classifiers.
Can improve the accuracy and
reduce the computation.
Comparison with EBL
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Test results reported in 1997.
Using two test sets (155 faces).
SVM achieved better detection rate
and fewer false alarms.
Recent approaches
Face segmentation/detection area
still remain active, for example:
An integrated SVM approach to multiface detection and recognition was
proposed in 2000.
 A technique of background learning was
proposed in August 2002.
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Still lots of potential!
Static face recognition
Numerous face recognition
methods/algorithms have been
proposed in last 20 years, several
representative approaches are:
Eigenface
 LDA/FDA
 Neural network (NN)
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Eigenface
The basic steps are:
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Registration. A face in an input image first
must be located and registered in a standardsize frame.
Eigenpresentation. Every face in the
database can be represented as a vector of
weights, the principal component analysis
(PCA) is used to encode face images and
capture face features.
Identification. This part is done by locating the
images in the database whose weights are
the closest (in Euclidean distance) to the
weights of the test images.
LDA/FDA
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Face recognition method using LDA/FDA is called the
fishface method.
Eigenface use linear PCA. It is not optimal to
discrimination for one face class from others.
Fishface method seeks to find a linear transformation to
maximize the between-class scatter and minimize the
within-class scatter.
Test results demonstrated LDA/FDA is better than
eigenface using linear PCA (1997).
Test results of LDA
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Test results of a subspace LDA-based
face recognition method in 1999.
Video-based Face Recognition
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Three challenges:
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Low quality
Small images
Characteristics of face/human objects.
Three advantage:
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Allows Provide much more information.
Tracking of face image.
Provides continuity, this allows reuse of
classification information from high-quality
images in processing low-quality images from
a video sequence.
Basic steps for video-based face recognition
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Object segmentation/detection.
Motion structure. The goal of this
step is to estimate the 3D depths
of points from the image sequence.
3D models for faces. Using a 3D
model to match frontal views of the
face.
Non-rigid motion analysis.
Recent approaches
Most video-based face recognition
system has three modules for
detection, tracking and recognition.
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An access control system using Radial Basis
Function (RBS) network was proposed in
1997.
A generic approach based on posterior
estimation using sequential Monte Carlo
methods was proposed in 2000.
A scheme based on streaming face
recognition (SFR) was propose in August
2002.
The SFR scheme
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Combine several decision rules together, such as
Discrete Hidden Markov Models (DHMM) and
Continuous Density HMM (CDHMM). The test
result achieved a 99% correct recognition rate in
the intelligent room.
Comparison
Two most representative and
important protocols for face
recognition evaluations:
 The
FERET protocol (1994).
Consists of 14,126 images of 1199 individuals.
 Three evaluation tests had been administered
in 1994, 1996, and 1997.
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 The
XM2VTS protocol (1999).
Expansion of previous M2VTS program (5
shots of each of 37 subjects).
 Now consists 295 subjects.
 The results of M2VTS/XM2VTS can be used
in wide range of applications.
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1996/1997 FERET Evaluations
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Compared ten algorithms.
Summary
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Significant achievements have been
made. LDA-based methods and NNbased methods are very successful.
FERET and XM2VTS have had a
significant impact to the developing of
face recognition algorithms.
Challenges still exist, such as pose
changing and illumination changing.
Face recognition area will remain active
for a long time.
Reference
[1] W. Zhao, R. Chellappa, A. Rosenfeld, and P.J. Phillips, Face Recognition: A Literature
Survey, UMD CFAR Technical Report CAR-TR-948, 2000.
[2] K. Sung and T. Poggio, Example-based Learning for View-based Human Face
Detection, A.I. Memo 1521, MIT A.I. Laboratory, 1994.
[3] H.A. Rowley, S. Baluja, and T. Kanade, Neural Network Based Face Detection, IEEE
Trans. On Pattern Analysis and Machine Intelligence, Vol. 20, 1998.
[4] E. Osuna, R. Freund, and F. Girosi, Training Support Vector Machines: An Application
to Face Recognition, in IEEE Conference on Computer Vision and Pattern Recognition,
pp. 130-136, 1997.
[5] M. Turk and A. Pentland, Eigenfaces for Recognition, Journal of Cognitive
Neuroscience, Vol.3, pp. 72-86, 1991.
[6] W. Zhao, Robust Image Based 3D Face Recognition, PhD thesis, University of
Maryland, 1999.
[7] K.S. Huang and M.M. Trivedi, Streaming Face Recognition using Multicamera Video
Arrays, 16th International Conference on Pattern Recognition (ICPR). August 11-15,
2002.
[8] P.J. Phillips, P. Rauss, and S. Der, FERET (Face Recognition Technology) Recognition
Algorithm Development and Test Report, Technical Report ARL-TR 995, U.S. Army
Research Laboratory.
[9] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, XM2VTSDB: The Extended
M2VTS Database, in Proceedings, International Conference on Audio and Video-based
Person Authentication, pp. 72-77, 1999.
Questions